IREE-Turbine is a package or toolset that combines PyTorch, Torch-MLIR, IREE, and additional tools to provide a comprehensive solution for compiling, optimizing, and executing PyTorch models using IREE's infrastructure. Based on the information in the image, IREE-Turbine offers the following key features:
1. AOT Export: This allows for Ahead-Of-Time compilation of PyTorch modules (nn.Modules) into deployment-ready artifacts. These compiled artifacts can then take full advantage of IREE's runtime features.
2. Eager Execution: It provides a torch.compile backend and a Turbine Tensor/Device for interactive PyTorch sessions. This enables users to work with PyTorch in a familiar environment while leveraging IREE's optimization capabilities.
3. Custom Ops: IREE-Turbine offers integration for defining custom PyTorch operations and implementing them using either IREE's backend IR or the Pythonic kernel language. This allows for extending PyTorch's functionality while maintaining compatibility with IREE's optimization pipeline.
In essence, IREE-Turbine acts as a bridge between PyTorch and IREE, allowing PyTorch users to benefit from IREE's advanced compilation and runtime features while maintaining a familiar PyTorch-based workflow. It aims to provide a seamless experience for compiling PyTorch models to run efficiently on various hardware targets supported by IREE.
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